Embretson, S.E., & Hershberger, S.L. The New
Rules of Measurement: What Every Psychologist and Educator Should
Know. Mahwah, NJ: Lawrence Erlbaum Associates, 1999.

This volume brings together leading measurement researchers
and practitioners to discuss new developments in test development,
administration, scoring, and interpretation. With topics ranging from
intelligence testing and personality assessment to validity issues
in testing theory and practice, psychometricians and clinical and
educational measurement specialists alike will find this book a useful
tool.

This volume addresses the issue of response error with
a book-length treatment of the theory and applications. It aims to
promote the use of statistical techniques that explicitly recognize
the presence of measurement error. The book begins with an introduction
to techniques for simple models, such as measurement variance known,
instrumental variable estimation, factor analysis, and others. Subsequent
chapters examine, in detail, vector explanatory variables, extensions
of the single relation model, and multivariate models.

The chapter focuses on issues of patient and treatment
measurement that would be encountered in an evaluation of an experimental
treatment or a novel therapeutic program. The first part of the chapter
deals with the rationale and methods associated with collecting patient
information at the start of a treatment intervention; the middle part
deals with the measurement of the intervention; and the last part
deals with the rationale for and the methodological issues in measuring
patient outcome following an intervention.

The chapters in this volume were selected from papers
presented at the Fifth International Objective Measurement Workshop,
held at UC Berkeley in 1989. The papers are grouped into three themes:
1. Measurement practice: how objective measurement methods are applied
to a variety of fields; 2. Measurement theory: development of new
measurement models that extend objective measurement into new domains;
and 3. Mathematical and statistical applications to measurement: mathematical
programming techniques, parameter estimation, and generalizability
theory.

The monograph arises from a technical review that was
conducted on September 8 and 9, 1994, in Gaithersburg, MD, where papers
were presented by 25 leading U.S. researchers on various aspects pertaining
to the validity of self-reported drug use. It reviews a number of
studies that use some presumably more accurate measure of drug use
to validate self-reported use. In addition, evolving methods to improve
a wide variety of procedures used in survey designs are explored,
including computer-assisted interviewing, predictors of response propensity,
measurement error models, and improved prevalence estimation techniques.
Experimental manipulations of various survey conditions and situational
factors also show promise in improving the validity of drug prevalence
estimates in self-report surveys.

The paper briefly describes the use of the CFA approach
in analyzing MTMM data and the inherent problems in such an approach.
The authors then present results of two studies, one with real data
and one with simulated data, that were set up to evaluate the performance
of the CFA methods for analyzing different types of MTMM data. The
correlated uniqueness model converges most of the time but the general
model only converges about one quarter of the times. The authors then
presents their recommendations for using the CFA approach in dealing
with MTMM problem.

The author provides a careful and illustrative review
of the principles of classical reliability theory. He also explores
some general strategies for improving measurement procedures. The
book begins with a presentation of random variables and the expected
values of a random variable. It then covers topics like the definition
of reliability as a coefficient and possible uses of a coefficient,
the notion of parallel tests so as to make possible the estimation
of a reliability coefficient for a set of measurements, what to do
when parallel tests are not available, what factors affect the reliability
coefficient, and how to estimate the standard error of measurement.

Zimmerman, D.W., & Williams, R.H. Note on the reliability
of experimental measures and the power of significance tests. Psychol
Bull 100:123-124, 1986.

The paper deals with the paradox that the power of a
statistical test sometimes increases and sometimes decreases as the
reliability coefficient of a dependent variable increases. The author
points out the relation between statistical power and the reliability
coefficient is not a functional relation unless another variableeither
true variance or error varianceremain constant.

The book offers an intuitive development of generalizability
theory, a technique for estimating the relative magnitudes of various
components of error variation and for indicating the most efficient
strategy for achieving desired measurement precision. The text covers
a variety of topics such as generalizability studies with nested facets
and with fixed facets, measurement error and generalizability coefficients,
and decision studies with same and with different designs.

The book provides a lucid but rigorous introduction
to the fundamental concepts of item response theory, followed by thorough,
accessible descriptions of the application of IRT methods to problems
in test construction, identification of potential biased test items,
test equating, and computerized-adaptive testing. A summary of new
directions in IRT research and development completes the book.

Aimed at helping researchers understand how item bias
methods work, this book provides practical advice and specific details
on the most useful methods for particular testing situations. Beginning
with a review of early bias methods and the fairness issues associated
with the topic of test bias, the authors explain the logic of each
method in terms of how differential item functioning (DIF) is defined
by the methodand how well the method can be expected to work
in various situations. In addition, chapters include a summary of
findings regarding the behavior of the various indices in empirical
studies, especially their reliability, correlation with known bias
criteria, and correlations with other bias methods. The book concludes
with a set of principles for deciding when DIF should be interpreted
as evidence of bias.